Change docs images to the 0.2.0 folder

doc/Architecture.rst

@@ -19,7 +19,7 @@ The napari-imagej menu provides buttons used to:
 
 The usage of these buttons is shown below:
 
-.. figure:: https://media.imagej.net/napari-imagej/menu_usage.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/menu_usage.png
 
     The ImageJ button in the napari-imagej toolbar launches the ImageJ user interface. From this interface, any ImageJ ecosystem routine can be executed, including third-party plugins. Data, including multi-channel images, can be passed between the napari and ImageJ interfaces using the transfer buttons, also located in the napari-imagej toolbar.
 
@@ -38,7 +38,7 @@ When the user selects a routine in the results tree, the result runner at the bo
 
 The usage of these components is shown below:
 
-.. figure:: https://media.imagej.net/napari-imagej/search_usage.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/search_usage.png
 
     Headless ImageJ ecosystem routines are executable directly from the napari interface by typing search terms into the napari-imagej search bar. A napari widget for executing a routine can then be generated by selecting any of the corresponding results shown in the panel beneath.
 
@@ -56,7 +56,7 @@ Without the first step, ImageJ would not understand the user's arguments. Withou
 
 napari-imagej implements both of these steps transparently as shown below, through the use of a type conversion layer. User inputs from Module widgets are converted to Java equivalents before the ImageJ routine is called. Routine outputs are converted to Python equivalents before those outputs are provided back to the user.
 
-.. figure:: https://media.imagej.net/napari-imagej/data_conversion.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/data_conversion.png
 
 To convert inputs, the data conversion layer maintains a list of conversion functions :math:`\{P_i:p_i\rightarrow j_i\}` used to convert objects of Python type :math:`p_i` into objects of Java type :math:`j_i`. As an example, one such function might convert napari ``Image`` layers into imglib2 ``Img``\s.
 

doc/Benchmarking.rst

@@ -35,7 +35,7 @@ Unfortunately, the data is stored within separate files for each focal plane and
 
    Then, we change the backing instance of napari-imagej to ``sc.fiji:fiji``; this gives us the Bio-Formats_ plugin, which will allow us to read in EmbryoCE.
 
-    .. figure:: https://media.imagej.net/napari-imagej/benchmarking_settings.png
+    .. figure:: https://media.imagej.net/napari-imagej/0.2.0/benchmarking_settings.png
 
         Recommended settings for napari-imagej benchmarking
 
@@ -52,7 +52,7 @@ Unfortunately, the data is stored within separate files for each focal plane and
 
     Once finished, Bio-Formats will import EmbryoCE into Fiji as a standard image. 
 
-    .. figure:: https://media.imagej.net/napari-imagej/benchmarking_focal_in_fiji.png
+    .. figure:: https://media.imagej.net/napari-imagej/0.2.0/benchmarking_focal_in_fiji.png
 
         EmbryoCE loaded as a single image in Fiji
 
@@ -117,7 +117,7 @@ We run the script using the following steps:
    * The other copy of ``focal`` to the ``output`` parameter
    * ``6.0`` to the ``sigma`` parameter.
 
-.. figure:: https://media.imagej.net/napari-imagej/benchmarking_setup_napari.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/benchmarking_setup_napari.png
 
     The expected napari-imagej benchmarking setup
 

doc/Configuration.rst

@@ -11,7 +11,7 @@ Accessing napari-imagej Settings
 
 As soon as you launch napari-imagej, you can access napari-imagej's configuration dialog by clicking on the gear in the napari-imagej menu:
 
-.. figure:: https://media.imagej.net/napari-imagej/settings_wheel.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/settings_wheel.png
 
     The configuration dialog is accessed through the gear button on the napari-imagej menu
 
@@ -91,7 +91,7 @@ Used to define command line arguments that should be passed to the JVM at startu
 
 One common use case for this feature is to increase the maximum heap space available to the JVM, as shown below:
 
-.. figure:: https://media.imagej.net/napari-imagej/benchmarking_settings.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/benchmarking_settings.png
 
     Specifying 32GB of memory available to ImageJ ecosystem routines in the JVM.
 

doc/Initialization.rst

@@ -14,7 +14,7 @@ Once triggered, napari-imagej will start up the JVM, and then the ImageJ2 gatewa
 
 **On the first initialization, napari-imagej must download an ImageJ2 distribution. This download can take minutes, depdending on the user's bandwidth.**
 
-.. figure:: https://media.imagej.net/napari-imagej/startup.gif
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/startup.gif
 
 Once napari-imagej is fully initilized, you can see the `Use Cases <./Use_Cases.html>`_ page for examples of available functionality. Alternatively, if you're new to ImageJ, you may want to start with a `high-level overview <https://imagej.net/learn/>`_.
 
@@ -27,7 +27,7 @@ While all ImageJ2 functionality should be accessible direclty through the napari
 
 If you try to run one of these commands through the napari-imagej search bar you will receive a message indicating the GUI is required, with an option to show it. Alternatively, at any point you can launch the ImageJ GUI via the GUI button in the napari-imagej menu.
 
-.. figure:: https://media.imagej.net/napari-imagej/settings_gui_button.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/settings_gui_button.png
 
     The GUI is launched through the ImageJ button on the napari-imagej menu
 

doc/Troubleshooting.rst

@@ -39,9 +39,9 @@ The Image Dimension Labels Are Wrong in ImageJ after Transferring from napari
 
 Internally, napari does not utilize image dimension labels (*i.e.* ``X``, ``Y``, ``Channel``, *etc...*) and instead assumes that the *n*-dimensional arrays (*i.e* images) conform to the `scikit-image dimension order`_ convention. ImageJ2 however *does* care about dimension labels and uses them to define certain operations. 
 
-For example, if you open the sample `live cell wide-field microscopy data of dividing HeLa cell nuclei <https://media.imagej.net/napari-imagej/trackmate_example_data.tif>`_ (which has the dimension order ``(X, Y, Time)`` in ImageJ's convention) in napari, transfer the data over to ImageJ2 with the napari-imagej transfer button and examine the properties of the image you will find that ImageJ2 has confused the ``Time`` dimension for ``Channel``. ImageJ2 thinks the transferred data has 40 channels instead of 40 frames. 
+For example, if you open the sample `live cell wide-field microscopy data of dividing HeLa cell nuclei <https://media.imagej.net/napari-imagej/0.2.0/trackmate_example_data.tif>`_ (which has the dimension order ``(X, Y, Time)`` in ImageJ's convention) in napari, transfer the data over to ImageJ2 with the napari-imagej transfer button and examine the properties of the image you will find that ImageJ2 has confused the ``Time`` dimension for ``Channel``. ImageJ2 thinks the transferred data has 40 channels instead of 40 frames. 
 
-.. figure:: https://media.imagej.net/napari-imagej/trackmate_adjust_props.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/trackmate_adjust_props.png
 
 The reason this happens is because ImageJ2 is not given dimension labels when data is transferred from napari. When ImageJ2 has no dimension label information for a given image then the ``(X, Y, Channel, Z, Time)`` dimension order and labels are applied to the image. In this example, the ``X`` and ``Y`` dimension labels are set properly, but the last dimension (which we know should be ``Time``) is set to ``Channel``. Note that this also means if your napari image has a shape that does conform to the scikit-image dimension order ``(t, pln, row, col, ch)`` it is possible that transferred images could be transposed into unintended orthogonal views of the data.
 

doc/Usage.rst

@@ -1,5 +1,5 @@
 Basic Usage
-===============
+===========
 
 napari-imagej offers two different mechanisms for accessing napari-imagej functionality - both are described below:
 
@@ -8,32 +8,27 @@ The napari-imagej widget
 
 The napari-imagej widget provides headless access to all ImageJ2 functionality, and all third-party plugins written in the ImageJ2 framework. These plugins can be found and run with the napari-imagej searchbar, as shown in the figure below:
 
-.. figure:: https://media.imagej.net/napari-imagej/gauss_search.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/gauss_search.png
 
     The napari-imagej widget, used to identify ImageJ functionality matching the search term "gauss"
 
 By clicking on an item in the search results, a set of actions is displayed at the bottom of the widget. To execute the selected functionality, users can click the ``Run`` button to launch a modal dialog, or they can click the ``Widget`` buton to launch a new napari widget (as shown below). Either button will allow the user to provide inputs to the ImageJ2 routine, and once the user confirms the selections the outputs of the routine will appear within the napari application.
 
 
-.. figure:: https://media.imagej.net/napari-imagej/gauss_widget.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/gauss_widget.png
 
     By clicking on the ``Widget`` button, a new napari widget is added to prompt the user for ImageJ2 routine input.
 
 .. |ImageJ2| image:: ../src/napari_imagej/resources/imagej2-16x16-flat.png
-   :height: 2 ex
-   :class: no-scaled-link
+
+.. NB: The svgs must have a fixed width to appear nicely inline
 
 .. |import| image:: ../src/napari_imagej/resources/import.svg
    :width: 15em
-   :class: no-scaled-link
-
 .. |export| image:: ../src/napari_imagej/resources/export.svg
    :width: 15em
-   :class: no-scaled-link
-
 .. |advanced export| image:: ../src/napari_imagej/resources/export_detailed.svg
    :width: 15em
-   :class: no-scaled-link
 
 
 Using the ImageJ2 UI
@@ -43,7 +38,7 @@ Many ImageJ ecosystem routines cannot be used headlessly - for this reason, napa
 
 To launch the ImageJ UI, press the |ImageJ2| button in the napari-imagej menu. Once the ImageJ UI is visible, ImageJ can be used as normal (to learn more, see `the ImageJ2 documentation <https://imagej.net/learn/>`__).
 
-.. figure:: https://media.imagej.net/napari-imagej/settings_gui_button.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/settings_gui_button.png
 
     The GUI is launched through the ImageJ button on the napari-imagej menu
 
@@ -57,7 +52,7 @@ To run ImageJ functionality through the ImageJ UI, users must export their data
 
 Note that these buttons are only enabled when there is a ``Layer`` that can be transferred.
 
-.. figure:: https://media.imagej.net/napari-imagej/v0.2.0/export_detailed_dialog.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/export_detailed_dialog.png
 
     Using the |advanced export| button, users can provide metadata for richer data transfer to the ImageJ UI
 

doc/examples/bonej.rst

@@ -40,7 +40,7 @@ Opening Data in napari
 
 Once you've unzipped the downloaded file, you can drag-and-drop the image onto napari to open it.
 
-.. figure:: https://media.imagej.net/napari-imagej/bonej2_open_data.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/bonej2_open_data.png
 
             A napari viewer showing the example image that we will be working with.
 
@@ -51,13 +51,13 @@ Now we need to process the image.
 
 First, we will blur the image to smooth intensity values and filter noise.
 
-.. figure:: https://media.imagej.net/napari-imagej/bonej2_select_gaussian.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/bonej2_select_gaussian.png
 
             Selecting the Gaussian menu entry from the nsbatwm's Tools menu.
 
 1. Within napari's menus choose: ``Tools > Filtering / noise removal > Gaussian (scikit image, nsbatwm)``
 
-.. figure:: https://media.imagej.net/napari-imagej/bonej2_gaussian_parameter.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/bonej2_gaussian_parameter.png
 
             Setting the parameters for the Gaussian.
 
@@ -67,7 +67,7 @@ First, we will blur the image to smooth intensity values and filter noise.
 
 Now we will threshold the image to make it binary.
 
-.. figure:: https://media.imagej.net/napari-imagej/bonej2_select_threshold.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/bonej2_select_threshold.png
 
             Selecting the thresholding algorithm developed by Li et al.
 
@@ -79,7 +79,7 @@ Now we will threshold the image to make it binary.
 
 4. Right click on the new ``Labels`` layer and select ``Convert to Image``. This will allow us to pass the result, now an ``Image`` layer, to BoneJ2.
 
-.. figure:: https://media.imagej.net/napari-imagej/bonej2_convert_to_image.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/bonej2_convert_to_image.png
 
             Converting the Labels layer to an Image layer for processing in BoneJ2.
 
@@ -103,7 +103,7 @@ Processing Data in napari with BoneJ2 and napari-imagej
 
 This will output the degree of anisotropy measurement for the image.
 
-.. figure:: https://media.imagej.net/napari-imagej/bonej2_anisotropy_parameters.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/bonej2_anisotropy_parameters.png
 
             Setting the parameters of BoneJ2's Anisotropy command.
 
@@ -122,7 +122,7 @@ This will output the degree of anisotropy measurement for the image.
 
 This will output the fractal dimension of the image.
 
-.. figure:: https://media.imagej.net/napari-imagej/bonej2_fractal_dimension.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/bonej2_fractal_dimension.png
 
             Setting the parameters of BoneJ2's fractal dimension command.
 
@@ -141,7 +141,7 @@ This will output the fractal dimension of the image.
 "Marching Cubes" that creates a surface mesh of the image before computing the surface area.
 This will output the surface area of the thresholded regions.
 
-.. figure:: https://media.imagej.net/napari-imagej/bonej2_surface_area.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/bonej2_surface_area.png
 
             Running BoneJ2's surface area command.
 
@@ -158,7 +158,7 @@ This will output the surface area of the thresholded regions.
 
 This will output the Bone Volume Fraction (BV/TV) measurement for the image.
 
-.. figure:: https://media.imagej.net/napari-imagej/bonej2_area_volume_fraction.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/bonej2_area_volume_fraction.png
 
             Running BoneJ2's area/volume fraction command.
 
@@ -175,7 +175,7 @@ This will output the Bone Volume Fraction (BV/TV) measurement for the image.
 
 This will output the Euler characteristic and Conn.D for the image.
 
-.. figure:: https://media.imagej.net/napari-imagej/bonej2_connectivity.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/bonej2_connectivity.png
 
             Running BoneJ2's connectivity command.
 
@@ -186,6 +186,6 @@ The final measurements
 We have now quantified our image with a number of methods and can use our resulting
 measurements in further scientific analysis!
 
-.. figure:: https://media.imagej.net/napari-imagej/bonej2_all_measurements.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/bonej2_all_measurements.png
 
             The results table for all of the BoneJ2 measurements. 

doc/examples/ops.rst

@@ -27,7 +27,7 @@ A `gaussian blur <https://en.wikipedia.org/wiki/Gaussian_blur>`_ is easily perfo
 
 Ops are searchable directly from the napari-imagej search bar. Therefore, looking for a gaussian blur is as simple as typing ``gauss`` into the search bar:
 
-.. figure:: https://media.imagej.net/napari-imagej/gauss_search.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/gauss_search.png
 
 From this version of ImageJ Ops, we can see three different Gaussian Blur Ops. Let's look at the first one:
 
@@ -51,13 +51,13 @@ We can run this Op by clicking on it, and then selecting one of the following bu
 
 Below we see the effect of pressing the ``Widget`` button:
 
-.. figure:: https://media.imagej.net/napari-imagej/gauss_widget.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/gauss_widget.png
 
 With this widget, we only need to enter in our inputs, and then press the run button. Note that napari will allow users to omit any parameters with a ``-----``.
 
 Below, we run this Op on a focal plane of the `EmbryoCE <https://samples.scif.io/EmbryoCE.zip>`_ image from https://samples.scif.io:
 
-.. figure:: https://media.imagej.net/napari-imagej/gauss_op.gif
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/gauss_op.gif
 
 .. _ImageJ Ops: https://imagej.net/libs/imagej-ops/index
 

doc/examples/scripting.rst

@@ -14,7 +14,7 @@ Configuration
 
 To run this use case, the following settings were used. For information on configuring napari-imagej, please see `here <../Configuration.html>`__.
 
-.. figure:: https://media.imagej.net/napari-imagej/settings_fiji.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/settings_fiji.png
 
     Configuration for the Puncta Segmentation use case
 
@@ -110,15 +110,15 @@ With napari-imagej running, the first step is to open the input data. We'll down
 
 The second step is to find our script within napari-imagej. Discovered SciJava Scripts can be found under their `filename <https://imagej.net/scripting/#adding-scripts-to-the-plugins-menu>`_; so we search for "puncta segmentation"
 
-.. figure:: https://media.imagej.net/napari-imagej/puncta_search.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/puncta_search.png
 
     ``Puncta_Segmentation.py`` exposed within the napari-imagej searchbar as ``PunctaSegmentation``.
 
 Double-clicking on ``PunctaSegmentation`` will bring a modal dialog, prompting the user for input data. The dialog also offers to display the resulting table in a new window, which may be preferred for large result tables.
 
 Once the "OK" button is clicked, the resuling table is displayed in a new window, or a new napari widget, based on the option you selected above:
 
-.. figure:: https://media.imagej.net/napari-imagej/puncta_results.png
+.. figure:: https://media.imagej.net/napari-imagej/0.2.0/puncta_results.png
 
 .. _Puncta Segmentation: https://pyimagej.readthedocs.io/en/latest/Puncta-Segmentation.html
 .. _SciJava Scripts: https://imagej.net/scripting/